The framework of GANs consists of a so-called “game” between two adversaries. One is the generator, whose objective is to generate samples which originate from the same distribution as some training data. The other is the discriminator, whose objective is to determine whether samples are “fake” (generated by the generator) or “real” (originating from the training set) [1]}. Goodfellow et al. liken the generator to a counterfeiter attempting to produce fake currency and distribute it undetected, and liken the discriminator to the police attempting to detect that fake currency [2]}. Competition in this process drives both the counterfeiter and the police to improve their methods of counterfeiting and detection, respectively, until the counterfeit currency is indistinguishable from the genuine article.
